R3f instance positions

Questions
1

When using instances we can specify the position of each one.

Is there a best practice way to put instances in specific locations.

In blender I would use a vertex group to do this.

In r3f I can think of maybe using a black and white mask or maybe defining some region to exclude my instances.

How do you guys deal with this problem?

2

here is a quick example of 10000 monkey heads using R3F

In a useEffect, use setMatrixAt

3

Thank you for your reply @seanwasere, either you didn’t understand my question or I didn’t explain it well enough.

I know how to do instancing and use setMatrixAt.

The problem I need help with, is determining how to show instances in some locations but not others, using a mask for example.

Say I have a landscape, and I only want trees on two hills that run down the side of a valley but not on the valley in-between those hills.

What’s the best way to do this really to get the coordinates needed for each instance?

4

You could likely use a surface sampler on the land geometry with painted vertecise to weight the distribution of trees (white = 1, black = 0)

1 Like
5

Hey @Lawrence3DPK

Thanks for the idea, so it inspired me to look into this a little more, and I ended up grabbing the image data from the texture, then using the npm package poisson-disk-sampling, to do some variation and avoid overlapping. Here’s my prototype for anyone else who wants to do similar or has a better idea.

import { useMemo } from 'react';
import PoissonDiskSampling from 'poisson-disk-sampling';

const getImageData = (texture) => {
  const canvas = document.createElement('canvas');
  const ctx = canvas.getContext('2d');
  const { width, height } = texture.image;

  // Set canvas size to match texture size
  canvas.width = width;
  canvas.height = height;

  // Draw the image onto the canvas
  ctx.drawImage(texture.image, 0, 0, width, height);

  // Extract pixel data
  return ctx.getImageData(0, 0, width, height).data;
}

export const Boxes = ({ maskTexture }) => {
  const positions = useMemo(() => {
    const data = getImageData(maskTexture);
    const width = maskTexture.image.width;
    const height = maskTexture.image.height;

    const sampler = new PoissonDiskSampling({
      shape: [width, height], // Define the area size
      minDistance: 60,        // Minimum distance between points
      tries: 30               // Attempts per point
    });

    const points = sampler.fill();
    const positions = [];

    points.forEach(([x, z]) => {
      const texX = Math.floor(x);
      const texY = Math.floor(z);
      const index = (texY * width + texX) * 4 + 3; // Access alpha channel
      const maskValue = data[index];

      if (maskValue === 255) {
        positions.push([(x / width) * 10 - 5, 0, (z / height) * 10 - 5]); // Scale to scene
      }
    });

    return positions;
  }, [maskTexture]);

  return (
    <>
      {positions.map((pos, idx) => (
        <mesh key={idx} position={pos}>
          <boxGeometry args={[0.3, 0.3, 0.3]} />
          <meshStandardMaterial color="orange" />
        </mesh>
      ))}
    </>
  );
}

test
test647×506 37.4 KB

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